3D Bayesian cluster analysis of super-resolution data reveals LAT recruitment to the T cell synapse
Research output: Contribution to journal › Article
Colleges, School and Institutes
- Department of Physics and Randall Division of Cell and Molecular Biophysics, King's College, London
- Department of Mathematics, Imperial College, London
- Advanced Imaging Center, Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, Virginia, USA
- Department of Medicine, Imperial College, London
- Department of Immunology, Infection and Inflammatory Disease, Kings College London, UK
- Department of Statistics, University of Oxford, Oxford OX1 3LB, UK.
Single-molecule localisation microscopy (SMLM) allows the localisation of fluorophores with a precision of 10-30 nm, revealing the cell's nanoscale architecture at the molecular level. Recently, SMLM has been extended to 3D, providing a unique insight into cellular machinery. Although cluster analysis techniques have been developed for 2D SMLM data sets, few have been applied to 3D. This lack of quantification tools can be explained by the relative novelty of imaging techniques such as interferometric photo-activated localisation microscopy (iPALM). Also, existing methods that could be extended to 3D SMLM are usually subject to user defined analysis parameters, which remains a major drawback. Here, we present a new open source cluster analysis method for 3D SMLM data, free of user definable parameters, relying on a model-based Bayesian approach which takes full account of the individual localisation precisions in all three dimensions. The accuracy and reliability of the method is validated using simulated data sets. This tool is then deployed on novel experimental data as a proof of concept, illustrating the recruitment of LAT to the T-cell immunological synapse in data acquired by iPALM providing ~10 nm isotropic resolution.
|Number of pages||9|
|Early online date||22 Jun 2017|
|Publication status||Published - 1 Dec 2017|